1. Field of the Invention
Claimed invention relates to processing of digital images, more concrete, to methods of creating a composite (mosaic) image from several partially overlapping images, captured by a flat-bed device such as a scanner or multi-functional peripheral (MFP).
2. Description of the Related Art
In general, a mosaic image is realized as an image, composed from large number of frames, partly overlapping and stitching together for receiving of single canvas. As a result, the mosaic image is typically larger than the maximum size of image, which could be scanned in one frame using of user's flat-bed device.
A number of technical decisions are known to generate such mosaic images. Early methods generally required a user input to determine overlap between images. However, more recent stitching methods can provide an automated aligning of images that cover random 2D field with images taken in an arbitrary order. In either case, as is well known to specialists, such systems use a set of images, captured by flatbed device and perform postprocessing such images, including, for example, aligning, compositing, blending of overlapped areas to generate a mosaic image which is then optionally cropped to a frame to create the final image mosaic.
From prior art the various methods for image generation, in which process of aligning is based on feature points matching. Among them there are methods based on cross-correlation of areas with the similar brightness. These methods are not invariant to scale and rotation of initial (input) images. Besides it was proposed to use various types of invariants, for example, Hu's and Flusser invariants (see J. Flusser and B. Zitová, “Combined invariants to linear filtering and rotation,” Intl. J. Pattern Recognition Art. Intell., Vol. 13, pp. 1123-1136, 1999) [1]. However, the most reliable method based on invariant features is Lowe's method (see Lowe, David G. (1999.) “Object recognition from local scale-invariant features”. Proceedings of the International Conference on Computer Vision 2: 1150-1157 [2]). Transforms, described in the given method, are geometrically invariant both in case of similarity transforms and affine transforms in brightness.
Invention, described in U.S. Pat. No. 6,097,418 [3], eliminates artifacts in an image formed using a plurality of initial elements. Visible seams in the image are eliminated by randomizing the stitch point between the scan lines produced by each imaging source. The randomization may be optimized by applying a method for relocating the random stitch point based on the data content of the scan line, adjacent scan lines, and other criteria. In the present invention the problem is also solved of compensation for scan errors caused by thermal factors, desynchronization of barrel, mechanical misalignment, and other factors associated with the use of a plurality of systems of creating of images. A photodetector system, comprising a mask having a pair of triangular openings, provides measurements of the errors inside the scanner.
In U.S. Pat. No. 7,271,822 [4] systems and methods are described for stitching multiple images together in a printer to form a single, seamless, composite image. The use of multiple laser sources and multiple scan lenses with one or more scanner devices and various image stitching methods allows achieving the much better quality of composite image, that at use of printers with single laser source and single scan lenses. Such benefits include, for example, a wider image format, smaller granularity, reduced cost, and reduced overall size for the printer.
In U.S. Pat. No. 7,006,111 [5] it is proposed to identify cases, when at least two digital images overlap at a first resolution level. At that it is achieved, that overlapping areas of the at least two digital images at a second resolution level higher than the first resolution level are obtained. At that the overlapping areas are identified at the second resolution level.
In U.S. Pat. Nos. 6,754,379 [6] and 6,359,617 [7] and in report Y. Xiong and K. Turkowski. “Registration, Calibration, and Blending in Creating High Quality Panoramas”. 4th IEEE Workshop on Applications of Computer Vision. Oct., 1998 [8] a method for aligning rectilinear images in 3D through projective record and calibration is offered. First, images are registered by projective method using gradient-based optimization and a correlation-based linear search. On the second step internal and external parameters of every image are calibrated using global optimization. This considerable minimizes overall image discrepancies in all overlap regions. On the third step images are blended using Laplacian-pyramid based method using blend mask generated by distance transform. Thus, smooth transition between images is provided and small residues of misalignment, resulting from parallax or imperfect pair-wise matching, are eliminated.
In spite of the fact that various program methods of creating of the mosaic image have been offered, nevertheless, a number of drawbacks could not get over by these methods. Among such drawbacks it is necessary to note incorrect blending of images and small speed of matching and blending of images for creating of the mosaic image.